The Monitoring with Advanced Sensors, Transmission and E-Resuscitation in Traumatic Brain Injury (MASTER-TBI) collaborative: bringing data science to the ICU bedside.

With the adoption of multimodal neuromonitoring techniques, a large amount of high resolution neurophysiological data is generated during the treatment of patients with moderate to severe traumatic brain injury (m-sTBI) that is available for further analysis. The Monitoring with Advanced Sensors, Transmission and E-Resuscitation in Traumatic Brain Injury (MASTER-TBI) collaborative was formed in 2020 to facilitate analysis of these data. The MASTER-TBI collaborative curates m-sTBI patient data for the purposes of comparative effectiveness research, machine learning algorithm development, and neuropathophysiological phenomena analysis. The MASTER-TBI collaborative is a multicentre longitudinal cohort study which utilises a novel hybrid cloud platform and other data science-informed techniques to collect and analyse data from patients with m-sTBI in whom both intracranial pressure monitoring and ICM+ (Cambridge Enterprise, Cambridge, UK) neuromonitoring software are utilised. MASTER-TBI enrols patients with m-sTBI from three participating Australian trauma intensive care units (ICUs). Captured outcome measures available for analysis include pathophysiological events (intracranial hypertension, cerebral perfusion pressure variations etc), surgical interventions, ICU and hospital length of stay, patient discharge status, and, where available, Glasgow Outcome Score-Extended (GOS-E) at 6 months. MASTER-TBI continues to develop data science-informed systems and techniques to maximise the use of captured high resolution m-sTBI patient neuromonitoring data. The highly innovative systems provide a world-class platform which aims to enhance the search for improved m-sTBI care and outcomes. This article provides an overview of the MASTER-TBI project's developed systems and techniques as well as a rationale for the approaches taken.